zlacker

>>laurex+(OP)
I don't understand the productivity that people get out of these AI tools. I've tried it and I just can't get anything remotely worthwhile unless it's something very simple or something completely new being built from the ground up.

Like sure, I can ask claude to give me the barebones of a web service that does some simple task. Or a webpage with some information on it.

But any time I've tried to get AI services to help with bugfixing/feature development on a large, complex, potentially multi-language codebase, it's useless.

And those tasks are the ones that actually take up the majority of my time. On the occasion that I'm spinning a new thing up quickly, I don't really need an AI to do it for me -- I mean, that's the easy part!

Is there something I'm missing? Am I just not using it right? I keep seeing people talk about how addictive it is, how the productivity boost is insane, how all their code is now written by AI and then audited, and I just don't see how that's possible outside of really simple rote programming.

>>brooke+Hm
The first and most important question to ask here is: are you using a coding agent? A lot of times, people who aren't getting much out of LLM-assisted coding are just asking Claude or GPT for code snippets, and pasting and building them themselves (or, equivalently, they're using LLM-augmented autocomplete in their editor).

Almost everybody doing serious work with LLMs is using an agent, which means that the LLM is authoring files, linting them, compiling them, and iterating when it spots problems.

There's more to using LLMs well than this, but this is the high-order bit.

>>tptace+Gv
Funny, I would give the absolute opposite advice. In my experience, the use of agents (mainly Cursor) is a sure-fire way to have a really painful experience with LLM-assisted coding. I much prefer to use AI as a pair programmer, that I talk to and sometimes get to write entire files, but I'm always the one doing the driving, and mostly the one writing the code.

If you aren't building up mental models of the problem as you go, you end up in a situation where the LLM gets stuck at the edges of its capability, and you have no idea how even to help it overcome the hurdle. Then you spend hours backtracking through what it's done building up the mental model you need, before you can move on. The process is slower and more frustrating than not using AI in the first place.

I guess the reality is, your luck with AI-assisted coding really comes down to the problem you're working on, and how much of it is prior art the LLM has seen in training.

>>lexand+CC
Agree. My favorite workflow has been chatting with the LLM in the assistant panel of Zed, then making inline edits by prompting the AI with the context of that chat. That way, I can align with the AI on how the problem should be solved before letting it loose. What's great about this depending on how easy or hard the problem is for the LLM, I can shift between handholding / manual coding and vibe coding.